Love this analogy. Thanks for the TL;DR summary. Breaks silently…and a user just leaves. Yep. Same as data. Except: in the worst possible moment they bring it up in a public forum 🫠
The boundary question is interesting - at what point does 'an agent that processes data' become a data product vs. just an automation? I'd argue the threshold is SLA: when you start treating the agent's outputs as something other teams depend on, with expectations around freshness, accuracy, and uptime, it's a data product.
The RAG + PGVector setup you mentioned from Alejandro is a good test case - if other teams are querying that output, it's a product whether or not you call it one. The ownership model that comes with that label is probably what matters most organizationally.
True Pawel, ownership on AI projects will be a super relevant discussion since right now most of them start as a side quest without too much definition, and that's where data & AI teams should work together mostly.
Love this analogy. Thanks for the TL;DR summary. Breaks silently…and a user just leaves. Yep. Same as data. Except: in the worst possible moment they bring it up in a public forum 🫠
You can have the best AI Agent until you show it in a demo 🤣
The boundary question is interesting - at what point does 'an agent that processes data' become a data product vs. just an automation? I'd argue the threshold is SLA: when you start treating the agent's outputs as something other teams depend on, with expectations around freshness, accuracy, and uptime, it's a data product.
The RAG + PGVector setup you mentioned from Alejandro is a good test case - if other teams are querying that output, it's a product whether or not you call it one. The ownership model that comes with that label is probably what matters most organizationally.
True Pawel, ownership on AI projects will be a super relevant discussion since right now most of them start as a side quest without too much definition, and that's where data & AI teams should work together mostly.